J. Takamatsu, K. OgawaraInstitute of Industrial Science
the University of Tokyo, Japan, H. KimuraGraduate School of Information Systems
the University of Electro-Communications
Tokyo, Japan, and K. IkeuchiGraduate School of Interdisciplinary Information Studies
the University of Tokyo, Japan

As one of the methods for reducing the work of programming, the
Learning-from-Observation (LFO) paradigm has been heavily promoted.
This paradigm requires the programmer only to perform a
task in front of a robot and does not require expertise. In this paper,
the LFO paradigm is applied to assembly tasks by two rigid polyhedral
objects. A method is proposed for recognizing these tasks as a
sequence of movement primitives from noise-contaminated data obtained
by a conventional 6 degree-of-freedom (DOF) object-tracking
system. The system is implemented on a robot with a real-time stereo
vision system and dual arms with dexterous hands, and its effectiveness
is demonstrated.